150 lines
2.4 KiB
YAML
150 lines
2.4 KiB
YAML
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# Deep Dive Relevance Keywords
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# Define keywords and their weights for scoring entries
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# Weight tiers: High (3.0x), Medium (1.5x), Low (0.5x)
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weights:
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high: 3.0
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medium: 1.5
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low: 0.5
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# High-priority keywords (critical to Hermes/Timmy work)
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high:
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# Framework specific
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- hermes
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- timmy
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- timmy foundation
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- langchain
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- langgraph
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- crewai
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- autogen
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- autogpt
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- babyagi
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# Agent concepts
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- llm agent
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- llm agents
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- agent framework
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- agent frameworks
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- multi-agent
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- multi agent
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- agent orchestration
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- agentic
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- agentic workflow
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- agent system
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# Tool use
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- tool use
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- tool calling
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- function calling
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- mcp
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- model context protocol
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- toolformer
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- gorilla
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# Reasoning
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- chain-of-thought
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- chain of thought
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- reasoning
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- planning
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- reflection
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- self-reflection
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# RL and training
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- reinforcement learning
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- RLHF
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- DPO
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- GRPO
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- PPO
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- preference optimization
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- alignment
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# Fine tuning
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- fine-tuning
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- finetuning
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- instruction tuning
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- supervised fine-tuning
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- sft
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- peft
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- lora
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# Safety
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- ai safety
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- constitutional ai
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- red teaming
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- adversarial
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# Medium-priority keywords (relevant to AI work)
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medium:
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# Core concepts
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- llm
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- large language model
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- foundation model
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- transformer
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- attention mechanism
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- prompting
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- prompt engineering
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- few-shot
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- zero-shot
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- in-context learning
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# Architecture
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- mixture of experts
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- MoE
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- retrieval augmented generation
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- RAG
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- vector database
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- embeddings
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- semantic search
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# Inference
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- inference optimization
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- quantization
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- model distillation
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- knowledge distillation
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- KV cache
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- speculative decoding
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- vLLM
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# Open research
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- open source
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- open weight
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- llama
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- mistral
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- qwen
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- deepseek
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# Companies
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- openai
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- anthropic
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- claude
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- gpt
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- gemini
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- deepmind
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- google ai
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# Low-priority keywords (general AI)
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low:
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- artificial intelligence
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- machine learning
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- deep learning
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- neural network
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- natural language processing
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- NLP
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- computer vision
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# Source-specific bonuses (points added based on source)
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source_bonuses:
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arxiv_ai: 0.5
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arxiv_cl: 0.5
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arxiv_lg: 0.5
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openai_blog: 0.3
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anthropic_news: 0.4
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deepmind_news: 0.3
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# Filter settings
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filter:
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min_relevance_score: 2.0
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max_entries_per_briefing: 15
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embedding_model: "all-MiniLM-L6-v2"
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use_embeddings: true
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